Human-Object Interaction Detection:A Quick Survey and Examination of Methods
It offers a foundational survey and analysis for researchers in computer vision, though it is incremental in its experimental contributions.
This paper provides the first general survey of human-object interaction detection methods and examines the performance of individual components in multi-stream convolutional neural network architectures, finding that spatial features contribute most to accuracy, with a 5% improvement over baseline models.
Human-object interaction detection is a relatively new task in the world of computer vision and visual semantic information extraction. With the goal of machines identifying interactions that humans perform on objects, there are many real-world use cases for the research in this field. To our knowledge, this is the first general survey of the state-of-the-art and milestone works in this field. We provide a basic survey of the developments in the field of human-object interaction detection. Many works in this field use multi-stream convolutional neural network architectures, which combine features from multiple sources in the input image. Most commonly these are the humans and objects in question, as well as the spatial quality of the two. As far as we are aware, there have not been in-depth studies performed that look into the performance of each component individually. In order to provide insight to future researchers, we perform an individualized study that examines the performance of each component of a multi-stream convolutional neural network architecture for human-object interaction detection. Specifically, we examine the HORCNN architecture as it is a foundational work in the field. In addition, we provide an in-depth look at the HICO-DET dataset, a popular benchmark in the field of human-object interaction detection. Code and papers can be found at https://github.com/SHI-Labs/Human-Object-Interaction-Detection.